Practical MATLAB deep learning : a project-based approach /

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. Youll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Paluszek, Michael
Otros Autores: Thomas, Stephanie
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress, 2020.
Temas:
MATLAB.
Machine learning.
Apprentissage automatique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Contents
  • About the Authors
  • About the Technical Reviewer
  • Acknowledgements
  • 1 What Is Deep Learning?
  • 1.1 Deep Learning
  • 1.2 History of Deep Learning
  • 1.3 Neural Nets
  • 1.3.1 Daylight Detector
  • Problem
  • Solution
  • How It Works
  • 1.3.2 XOR Neural Net
  • Problem
  • Solution
  • How It Works
  • 1.4 Deep Learning and Data
  • 1.5 Types of Deep Learning
  • 1.5.1 Multilayer Neural Network
  • 1.5.2 Convolutional Neural Networks (CNN)
  • 1.5.3 Recurrent Neural Network (RNN)
  • 1.5.4 Long Short-Term Memory Networks (LSTMs)
  • 1.5.5 Recursive Neural Network
  • 1.5.6 Temporal Convolutional Machines (TCMs)
  • 1.5.7 Stacked Autoencoders
  • 1.5.8 Extreme Learning Machine (ELM)
  • 1.5.9 Recursive Deep Learning
  • 1.5.10 Generative Deep Learning
  • 1.6 Applications of Deep Learning
  • 1.7 Organization of the Book
  • 2 MATLAB Machine Learning Toolboxes
  • 2.1 Commercial MATLAB Software
  • 2.1.1 MathWorks Products
  • Deep Learning Toolbox
  • Instrument Control Toolbox
  • Statistics and Machine Learning Toolbox
  • Computer Vision System Toolbox
  • Image Acquisition Toolbox
  • Parallel Computing Toolbox
  • Text Analytics Toolbox
  • 2.2 MATLAB Open Source
  • 2.2.1 Deep Learn Toolbox
  • 2.2.2 Deep Neural Network
  • 2.2.3 MatConvNet
  • 2.2.4 Pattern Recognition and Machine Learning Toolbox (PRMLT)
  • 2.3 XOR Example
  • 2.4 Training
  • 2.5 Zermelo's Problem
  • 3 Finding Circles with Deep Learning
  • 3.1 Introduction
  • 3.2 Structure
  • 3.2.1 imageInputLayer
  • 3.2.2 convolution2dLayer
  • 3.2.3 batchNormalizationLayer
  • 3.2.4 reluLayer
  • 3.2.5 maxPooling2dLayer
  • 3.2.6 fullyConnectedLayer
  • 3.2.7 softmaxLayer
  • 3.2.8 classificationLayer
  • 3.2.9 Structuring the Layers
  • 3.3 Generating Data: Ellipses and Circles
  • 3.3.1 Problem
  • 3.3.2 Solution
  • 3.3.3 How It Works
  • 3.4 Training and Testing
  • 3.4.1 Problem
  • 3.4.2 Solution
  • 3.4.3 How It Works
  • 4 Classifying Movies
  • 4.1 Introduction
  • 4.2 Generating a Movie Database
  • 4.2.1 Problem
  • 4.2.2 Solution
  • 4.2.3 How It Works
  • 4.3 Generating a Movie Watcher Database
  • 4.3.1 Problem
  • 4.3.2 Solution
  • 4.3.3 How It Works
  • 4.4 Training and Testing
  • 4.4.1 Problem
  • 4.4.2 Solution
  • 4.4.3 How It Works
  • 5 Algorithmic Deep Learning
  • 5.1 Building a Detection Filter
  • 5.1.1 Problem
  • 5.1.2 Solution
  • 5.1.3 How It Works
  • 5.2 Simulating Fault Detection
  • 5.2.1 Problem
  • 5.2.2 Solution
  • 5.2.3 How It Works
  • 5.3 Testing and Training
  • 5.3.1 Problem
  • 5.3.2 Solution
  • 5.3.3 How It Works
  • 6 Tokamak Disruption Detection
  • 6.1 Introduction
  • 6.2 Numerical Model
  • 6.2.1 Dynamics
  • 6.2.2 Sensors
  • 6.2.3 Disturbances
  • 6.2.4 Controller
  • 6.3 Dynamical Model
  • 6.3.1 Problem
  • 6.3.2 Solution
  • 6.3.3 How It Works
  • 6.4 Simulate the Plasma
  • 6.4.1 Problem
  • 6.4.2 Solution
  • 6.4.3 How It Works
  • 6.5 Control the Plasma
  • 6.5.1 Problem
  • 6.5.2 Solution
  • 6.5.3 How It Works
  • 6.6 Training and Testing
  • 6.6.1 Problem
  • 6.6.2 Solution

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